Most of the current complex networks that are of interest to practitioners possess a certain
community structure that plays an important role in understanding the properties of these …

Rare or limited access to real-world data has widely been a stumbling block for the
development and employment of design optimization and simulation models in water …

Abstract The Parameterized Algorithms and Computational Experiments challenge (PACE)
2022 was devoted to engineer algorithms solving the NP-hard Directed Feedback Vertex …

Many applications require randomly sampling bipartite graphs with fixed degrees or
randomly sampling incidence matrices with fixed row and column sums. Although several …

A (fully) dynamic graph algorithm is a data structure that supports edge insertions, edge
deletions, and answers specific queries pertinent to the problem at hand. In this work, we …

We consider the following common network analysis problem: given a degree sequence
d=(d1,…, dn)∈ ℕ n return a uniform sample from the ensemble of all simple graphs with …

Preferential attachment lies at the heart of many network models aiming to replicate features
of real world networks. To simulate the attachment process, conduct statistical tests, or …

Shuffling is the process of rearranging a sequence of elements into a random order such
that any permutation occurs with equal probability. It is an important building block in a …

The uniform sampling of simple graphs matching a prescribed degree sequence is an
important tool in network science, eg to construct graph generators or null-models. Here, the …

Network models are developed and used in various fields of science as their design and
analysis can improve the understanding of the numerous complex systems we can observe …